Systems thinking in health technology assessment: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Our objective was to assess how, and to what extent, a systems-level perspective is considered in decision-making processes for health interventions by illustrating how studies define the boundaries of the system in their analyses and by defining the decision-making context in which a systems-level perspective is undertaken. METHOD: We conducted a scoping review following the Joanna Briggs Institute methodology. MEDLINE, EMBASE, Cochrane Library, and EconLit were searched and key search concepts included decision making, system, and integration. Studies were classified according to an interpretation of the "system" of analysis used in each study based on a four-level model of the health system (patient, care team, organization, and/or policy environment) and using categories (based on intervention type and system impacts considered) to describe the decision-making context. RESULTS: A total of 2,664 articles were identified and 29 were included for analysis. Most studies (16/29; 55%) considered multiple levels of the health system (i.e., patient, care team, organization, environment) in their analysis and assessed multiple classes of interventions versus a single class of intervention (e.g., pharmaceuticals, screening programs). Approximately half (15/29; 52%) of the studies assessed the influence of policy options on the system as a whole, and the other half assessed the impact of interventions on other phases of the disease pathway or life trajectory (14/29; 48%). CONCLUSIONS: We found that systems thinking is not common in areas where health technology assessments (HTAs) are typically conducted. Against this background, our study demonstrates the need for future conceptualizations and interpretations of systems thinking in HTA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.203 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.045 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".